Motor disability is a neuro-degenerative disorder in which brain nerve cells are damaged by brain strokes, spinal cord injuries (SCIs), accidents, etc. This interrupts the communication between the brain and various parts of the body and makes the person paralyzed. Brain-computer interface (BCI) is one of the most advanced and demanding techniques that enable motor-disabled people to communicate with the external environment via their brain signals. In order to perform effective communication, a robust BCI system needs to be developed, which should be able to detect the thoughts or intentions of the patients. Such a system contains a powerful algorithm that translates the patient’s thoughts or intentions into control signals for effective communication. In past, various powerful algorithms have been proposed to improve the efficiency of BCIs. These algorithms have two major drawbacks: firstly, most algorithms rely on the features extracted from multi-channel data acquisition systems. The more the number of channels is the more complexity of the algorithms and computational time, which reduces the system’s efficiency. In addition to this, the multi-channel data acquisition system is expensive, and therefore, patients in rural areas cannot afford it. Secondly, several algorithms deal with the feature extraction of brain signals and their classification. They did not focus on the dynamical behavior of the brain signals.

At present, the research is primarily focused on minimizing these two drawbacks of the literature. The main objective of current research is to use the minimum number of channels to extract the maximum information from the human brain. Therefore, in this research work, only two channels (C3 and C4) are used to investigate the behavior of the brain activities at rest and in movement states. To progress in these directions, both linear and phase plot techniques are employed to explore brain activities during motor imagery (MI) movements. The linear methods included the Inter Trial Variance (IV), Hilbert Transforms (HT) and Common Spatial Pattern (CSP). On the other hand, the dynamical behavior of the brain activities is well addressed by the phase plot techniques such as Second-Order Difference Plots (SODP) and Phase Space Reconstruction (PSR). The research is carried out on two different datasets; one is obtained from the BCI competitions website and the other is recorded from the Biomedical Instrumentation and Signal Processing Laboratory, India Institute of Technology, Madras, India. The BCI competitions dataset contains raw electroencephalogram (EEG) of normal subjects performing left and right hand MI movements. On the other hand, the lab-recorded dataset contains EEG signals of both normal and paraplegic subjects imagining left and right-hand movements. The result depicts that both linear and phase plot techniques graphically discriminate LH and RH MI movement of the subjects and the performance of the proposed techniques is higher than the state-of-the-art techniques.